Published online before print
January 14, 2003, 10.1101/gr.912603
Vol 13, Issue 2, 216-223, February 2003
LETTER
Global RNA Half-Life Analysis in Escherichia coli Reveals Positional Patterns of Transcript Degradation
Douglas W. Selinger1,3,
Rini Mukherjee Saxena2,3,
Kevin J. Cheung1,
George M. Church1 and
Carsten Rosenow2,4
1Harvard Medical School, Department of Genetics,
Boston, Massachusetts 02115, USA; 2Affymetrix Inc.,
Santa Clara, California 95051, USA
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ABSTRACT
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Subgenic-resolution oligonucleotide microarrays were used to study
global RNA degradation in wild-type Escherichia coli MG1655.
RNA chemical half-lives were measured for 1036 open reading frames
(ORFs) and for 329 known and predicted operons. The half-life of total
mRNA was 6.8 min under the conditions tested. We also observed
significant relationships between gene functional assignments and
transcript stability. Unexpectedly, transcription of a single operon
(tdcABCDEFG) was relatively rifampicin-insensitive and showed
significant increases 2.5 min after rifampicin addition. This supports
a novel mechanism of transcription for the tdc operon, whose
promoter lacks any recognizable  binding sites. Probe by probe
analysis of all known and predicted operons showed that the 5' ends of
operons degrade, on average, more quickly than the rest of the
transcript, with stability increasing in a 3' direction, supporting and
further generalizing the current model of a net 5' to 3' directionality
of degradation. Hierarchical clustering analysis of operon degradation
patterns revealed that this pattern predominates but is not exclusive.
We found a weak but highly significant correlation between the
degradation of adjacent operon regions, suggesting that stability is
determined by a combination of local and operon-wide stability
determinants. The 16 ORF dcw gene cluster, which has a complex
promoter structure and a partially characterized degradation pattern,
was studied at high resolution, allowing a detailed and integrated
description of its abundance and degradation. We discuss the
application of subgenic resolution DNA microarray analysis to study
global mechanisms of RNA transcription and processing.
Gene regulation is a dynamic process which can be
controlled by a number of mechanisms as genetic information flows from
nucleic acids to proteins. The study of gene regulation in the steady
state, while informative, overlooks the underlying dynamics of the
processes. Steady-state transcript levels are a result of both RNA
synthesis and degradation, and as such, measurements of degradation
rates can be used to determine their rates of synthesis (if their
steady-state levels are known) as well as reveal regulation which
occurs via changes in RNA stability.
For the genetic regulatory network of Escherichia coli to be
understood and eventually modeled, all means of regulation in use by
the cell must be given due attention. RNA degradation in eubacteria was
once viewed as a nonspecific, unregulated process. Today it is known to
involve multiple degradation pathways, a multisubunit protein complex
(the degradosome), and to be an important regulatory mechanism for the
expression of some genes (for reviews, see Grunberg-Manago 1999 ; Rauhut
and Klug 1999; Regnier and Arraiano 2000). A small number of
large-scale RNA degradation analyses have recently been reported in
budding yeast (Wang et al. 2002), humans (Lam et al. 2001), and E.
coli (Bernstein et al. 2002).
RNA expression analysis with DNA microarrays has allowed transcription
to be studied at an unprecedented scale. Nevertheless, the potential of
the technology to elucidate the low-level details of the transcription
and processing of RNA has been poorly explored. In this study we have
taken a first step by identifying global RNA degradation patterns at
the operonic, genic, and subgenic levels.
High-density oligonucleotide arrays from Affymetrix were used to study
the degradation of RNA over essentially the entire transcriptome of
E. coli MG1655 (Selinger et al. 2000). These arrays have
subgenic-resolution coverage of the genome (both coding and noncoding
regions), allowing us to examine transcription and degradation in a
relatively continuous and unbiased manner.
We present RNA half-life measurements for 1036 open reading frames
(ORFs) and for 329 known and predicted operons. We present significant
over- and underrepresentation of ORF functional categories in the set
of most labile RNAs. We identify an unusual rifampicin-insensitive
promoter (of the tdc operon) and strengthen the case for its
transcription by a novel mechanism. We present evidence for the higher
lability of the 5' ends of operons relative to their 3' ends,
supporting the current model of an overall 5' to 3' direction of
degradation. Finally, we explore positional patterns of RNA degradation
and discuss the current state of the art of high-resolution global
transcription analysis.
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RESULTS AND DISCUSSION
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Half-Life Determination
For the determination of half-lives, all experiments were done in
triplicate for each RNA preparation. On average, 23% of the genes were
detected at 2.33 (99% confidence) above negative control probe
sets. Half-lives were calculated for 1036 ORFs, of which 479 were
calculated exactly and 557 represent upper bounds. Average half-lives
were calculated for 329 known and predicted operons (Tables
1,2; see
Methods), although these are only a rough approximation, as typically
only a subset of the ORFs had measurable half-lives, and there can be
considerable differences between the degradation of different operonic
regions.
After addition of rifampicin, which prevents initiation of new
transcripts by binding to the  subunit of RNA polymerase (Campbell
et al. 2001), the total intensity for all mRNAs decreases exponentially
with time (R = 0.98) with an estimated overall chemical half-life of
6.8 min. This is in rough agreement with a reported half-life of 7.5
min for total pulse-labeled RNA in comparable conditions (Mohanty and
Kushner 1999). Although absolute decay rates are known to vary
appreciably across experiments, especially those determined in
different laboratories, we observe qualitative agreement with some well
studied transcripts, such as ompA, a very stable RNA in
fast-growing cells (Nilsson et al. 1984; see Methods), and
cspA, an extremely unstable one which is transiently
stabilized upon cold shock (Table 1; Goldenberg et al. 1996).
Genes encoding enzymes known to be involved in RNA decay such as
pnp, rhlB, and rho show exponential decay
patterns starting immediately after rifampicin treatment. The genes
rne and rnc also show progressive decay patterns but
were expressed at relatively low levels, making half-life measurement
difficult. The genes rnb and pcnB were undetected
throughout the timecourse.
Average operon half-lives were calculated by taking the mean of the
operons' member ORFs for which half-lives had been determined. A
number of the most unstable operons (Table 2) enable metabolism that is
presumably unnecessary in rich media, such as amino acid biosynthesis
(thr, cad), alternative carbon source catabolism (lac, sdh), and
nucleotide biosynthesis (deo). It would be interesting to see whether
these transcripts are more stable in rich media.
Discovery of a Rifampicin-Insensitive Promoter
Surprisingly, a single operon, tdcABCDEFG, which encodes a
pathway for the transport and anaerobic degradation of L-threonine, was
relatively rifampicin-insensitive. All seven ORFs of this operon were
significantly upregulated at 2.5 min after rifampicin addition. After
their initial increase at 2.5 min, the ORFs of the tdc operon
show either gradual decay or stability through the 5- and 10-min
timepoints, followed by near-complete degradation by the 20-min
timepoint (data not shown). Because rifampicin targets the core of the
only RNA polymerase (RNAP) in E. coli, we were initially
surprised to find an operon which could still be transcribed after
rifampicin addition. However, differential sensitivity to rifampicin by
RNAP holoenzyme containing different subunits (70 vs.
32) was observed previously (Wegrzyn et al. 1998),
suggesting that certain holoenzymes may be rifampicin-insensitive.
Furthermore, the tdc promoter is unusual in that it doesn't
contain any recognizable binding sites, but does contain sites for
a number of transcription factors, including CRP, IHF, FNR, LysR, TdcA,
and TdcR. It has also been suggested that the tdc promoter is
controlled by a novel mechanism and can be activated by altering its
local topology (Wu and Datta 1995; Sawers 2001).
RNA Decay Related to Function
To determine whether transcripts whose gene products participate in
the same cellular processes tended to be degraded at the same rates, we
looked at the over- and underrepresentation of 23 gene functional
categories (Blattner et al. 1997) within different half-life ranges
(Table 3). P-values were
calculated using the cumulative hypergeometric distribution, and a 95%
confidence level was used as a cutoff (Tavazoie et al. 1999). In the
set of short-lived ( 5-min) transcripts, genes annotated as putative
enzymes were significantly overrepresented. Rapidly degraded
transcripts are good candidates for regulation via RNA stability, and
many of these may be transiently stabilized in some environmental
condition in which they are needed. The instability of their
transcripts, and likely low protein levels, may have been a hindrance
to their discovery and/or characterization. Genes involved in
translation and posttranslational modification were significantly
underrepresented among short-lived (5-min) transcripts, reflecting
the known stability of the cell's translational machinery. Genes
involved in energy metabolism were significantly overrepresented among
transcripts with intermediate half-lives of between 10 and 20 min. The
genes in this category are, in general, well studied and are regulated
by a variety of mechanisms unrelated to RNA stability, although in most
cases regulation via transcript stability has not been ruled out.
To assess whether our experiment preferentially measured the half-lives
of some groups of genes relative to others, we looked for differential
representations of genes with measured half-lives relative to all genes
on the array. Genes whose half-lives could be determined in our
experiment were significantly overrepresented for those involved in
translation and posttranslational modification, which are generally
very highly expressed and easy to detect. Those classified as
"hypothetical, unclassified, unknown" or as putative transport
proteins were significantly underrepresented, suggesting that both of
these classes in general are expressed at a very low level and/or may
contain a number of spuriously predicted ORFs. These two
uncharacterized groups stand in contrast to putative enzymes and
putative regulatory proteins, which were detected at a rate
indistinguishable from those of other groups.
5' to 3' Directionality of Degradation
RNA is degraded within the cell by the combined action of RNA exo-
and endonucleases. The precise way in which this process occurs has
been a subject of intense study (Grunberg-Manago 1999; Regnier and
Arraiano 2000). Stable 5' secondary structures have been shown to
confer stability on downstream sequences (Emory et al. 1992), whereas
3' polyadenylation targets transcripts for degradation (Sarkar 1997).
To investigate whether degradation is targeted preferentially towards
the 5' or 3' end of the mRNA, we measured the variability of
degradation rates at different positions of predicted and known operons
containing at least two ORFs. Each operon coding region was divided
into three equal regions (5', middle, and 3'), whereas 30 bases
upstream and downstream of the operons were denoted 5' and 3' UTRs,
respectively. The UTR was chosen to be relatively short to increase the
probability that it was in fact cotranscribed with the operon. The
average log2 ratio of probes in each region was calculated
for each operon (see Methods).
Log2 ratios of each region were averaged for all operons, as
well as for subsets with specified half-lives, to compare the
degradation rates of different transcript regions (Fig.
1). In the set of all operons, the
log2 ratios were most negative for the 5' UTR and became less
negative in a 5' to 3' direction, consistent with a predominantly 5' to
3' directional mechanism of degradation.
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Figure 1. Positional differences in operon degradation. Operon regions are
plotted on the x-axis, and average log2 ratios
(compared to the 0 min timepoint) are plotted on the y-axis.
Vertical bars indicate standard error. Operons were divided into five
regions: 30 bases upstream (5p UTR) and downstream (3p UTR), and three
equal-length regions of the coding region: 5 prime (Op 5p), middle (Op
M), and 3 prime (Op 3p). Patterns of operons with different average
half-lives were compared. A 5' to 3' directionality is observable in
the coding regions of all operon subsets. This directionality generally
extends at least 30 bases into the UTRs, although the 5' UTR of quickly
degrading operons (<5 min) seems to be more stable than the coding
region. All curves in this figure have significant variation between
means by one-way ANOVA at  = 0.001, with the following exceptions:
2.5 min of the ‘20–40 min’ graph, and the 5- and 20-min curves of
the ‘half-life not determined’ graph, which were significant at
= 0.05, 0.05, and 0.10, respectively. P-values for
timepoints on the ‘all operons’ graph were all below
1x10–12.
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To determine whether positional patterns varied depending on overall
stability, operons were grouped based on their average half-lives (Fig.
1). The same trend of 3'-increasing stability was seen for all groups,
regardless of overall half-life. This trend was most consistent for the
20–40-min operons, whereas for the <5 min and the 5–20-min operons,
there were some discrepancies at their 5' ends, especially at the later
timepoints.
To assess the significance of the differential degradation rates, we
used a one-way ANOVA to test whether the differences between average
degradation rates of different operon regions could be accounted for by
chance. Significant differences between regional mean degradation rates
were found for almost all timepoints in all half-life sets using
= 0.001. Three cases were significant only at = 0.05 or
0.10, as detailed in the Figure 1 legend. The results for the analysis
of all 835 operons were especially significant, with all
P-values below 1x10–12. We conclude that the
observed variation in the rate of degradation of different operonic
regions is significant.
Clustering of Degradation Patterns
It is important to note that although the 5' to 3' directionality
illustrated by Figure 1 indicates that in general, the 5' ends of
operons are degraded more quickly than their 3' ends, it does not
indicate whether this is the only pattern of operon degradation, or
simply the most common one. To distinguish between these two
possibilities, the degradation patterns of all operons were clustered
using a hierarchical clustering algorithm and displayed as a tree (Fig.
2; Eisen et al. 1998). Here, 149 known and
predicted operons for which complete data was available were divided
into five operon regions: 5' and 3' UTR (representing 30 bases up- and
downstream of the translation start and stop, respectively), and
equal-length 5', middle, and 3' coding regions. Within each operon,
each region was ranked from most stable (5) to least stable (1) based
on the average log2 ratio of oligos in that region at each
timepoint. This within-operon normalization allows operons with similar
patterns to be grouped together regardless of their overall rate of
degradation. The results of the clustering analysis indicate that
although there is a clear predominance of a 5' to 3' degradation
pattern, other patterns are also present. Nevertheless, the degradation
ranks for each region, when averaged over all operons, show a clear
trend consistent with an overall 5' to 3' directionality of
degradation.
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Figure 2. Whole-genome cluster analysis of operon degradation. The degradation
patterns of 149 operons (containing two or more ORFs, and oligo probes
in all targeted regions) were hierarchically clustered after ranking
the relative degradation rate of each region. The algorithm was
implemented using the GeneCluster/TreeView package (Eisen et al. 1998).
Transcript regions are on the x-axis, with each region split
into 2.5-, 5-, 10-, and 20-min timepoints. The average rank increases
from 5' to 3', supporting a predominant 5' to 3' directionality of
degradation (cluster c). The clustering also reveals that a variety of
degradation patterns are present, such as operons with relatively
stable 5' UTRs (cluster a). One group of operons (cluster b) is
initially degraded most quickly at its 3' UTR at 2.5 and 5 min, but
then by the 10 min timepoint is more quickly degraded at its middle and
3' coding regions.  2 goodness of fit tests show that the
distributions of degradation ranks are highly nonrandom, with 5'
regions more likely to be degraded quickly and 3' regions more likely
to be degraded slowly. The complete clustering file, including gene
names, is available at http://arep.med.harvard.edu/rna_decay/.
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To assess the statistical significance of the observed directionality,
we performed a 2 goodness of fit test on each transcript
region. We are easily able to reject the null hypothesis that each
region has an equiprobable distribution of ranks, with
P-values ranging from 2x10–6 to
2x10–38 (Fig. 2). From inspection of the rank distributions
we conclude that 5' regions of operons are significantly more likely to
be degraded quickly and 3' regions more likely to be degraded slowly.
Because certain transcript features, such as the ompA
stabilizer (Emory et al. 1992), are known to exert their effects along
an entire transcript, we analyzed the extent to which the degradation
of one region is correlated to other regions. The average Pearson's
linear correlation coefficient (R) between the degradation of adjacent
regions was 0.38, and the average correlation between any two operon
regions was 0.26. These weak but statistically significant
(P < 0.005) correlations suggest that although there are
important operon-wide determinants of stability, local determinants may
play a larger role in the stability of RNAs. This emphasizes the need
to scrutinize transcription and degradation at a higher level of
resolution.
It should be noted that despite the difficulties of defining transcript
boundaries, as well as the existence of operons with multiple promoters
and terminators, we were still able to identify significant patterns.
As our knowledge of these confounding factors increases, we may expect
to see even clearer patterns emerge.
High-Resolution Analysis of the dcw Gene Cluster
The dcw gene cluster, important for cell envelope
biosynthesis and cell division, contains 16 ORFs and has a complex
promoter structure (Fig. 3; Vicente et al.
1998; Dewar and Dorazi 2000). It is transcribed mainly from two
clusters of promoters located at the 5' end ( ORFs 1–3), and near
the 3' end (ORFs 12–14). We observe a complex degradation pattern for
this operon, with three primary domains of stability (Figs.
3,4). The 5' end is degraded most rapidly,
consistent with the most commonly observed pattern. The central region
is relatively stable from murE to murC. The 3' end,
from ddlB to envA, has an intermediate stability,
with ftsA and ftsZ having nearly identical
half-lives, as has been reported previously (Cam et al. 1996).
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Figure 4. Transcript abundance and degradation of the dcw gene cluster.
The dcw gene cluster contains 16 ORFs involved in cell
envelope biosynthesis and cell division. Several promoters have been
described (see Fig. 3), and it is likely that they are all used, to
varying extents. It has also been speculated that the cluster may
sometimes be transcribed in its entirety. The ORFs were plotted here in
the order they are transcribed, showing their array signal intensities
(average differences) throughout the timecourse. Although average
difference is only an approximate indicator of transcript abundance,
relatively high levels of steady-state RNA are observed downstream of
the mraZ and ddlB promoters, at the 5' end and about
two-thirds of the way into the transcript, respectively. The middle
portion of the operon has lower steady-state RNA levels and is
degraded more slowly (see Fig. 3).
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These domains of stability roughly coincide with the clusters of
promoters, suggesting that they represent somewhat independent units
which the cell chooses to regulate simultaneously by both
transcriptional initiation and degradation. Interestingly, the
relatively high signal intensity at mraZ and ddlB
corresponds to the positions of the two major promoters Pmra
and ftsQ2p1p, respectively (Fig. 4; Flardh et al. 1997;
Mengin-Lecreulx et al. 1998). This suggests that the regions downstream
of these promoters are maintained at higher steady-state RNA levels in
the cell, although we are cautious about making a firm conclusion in
this regard due to the only semiquantitative nature of the relationship
between microarray signal intensity and absolute RNA abundance.
Nevertheless, this observation is consistent with previous measurements
which show that about one-third of the transcription of ftsZ
originates at promoters located within and between ddlB and
ftsA, with the other two-thirds originating upstream of
ddlB (Flardh et al. 1998; de la Fuente et al. 2001).
The Future of High-Resolution Transcriptome Analysis
The type of transcriptome data presented here enables genome-wide
analyses, which until now have only been done on a small scale. For
example, the relationship between RNA degradation and RNA sequence
features such as RNase sites and known and predicted secondary
structures can be assessed, as well as the effects of mutations,
especially to the RNA degradation machinery. These data are also useful
in the empirical definition of transcription boundaries (Selinger et
al. 2000; Tjaden et al. 2002) and promoter usage.
We expect such high-resolution analyses to increase in precision.
Probe-to-probe variation, which can mask local changes in RNA
abundance, can be improved by smoothing or, perhaps, by more
sophisticated model-based (Li and Hung Wong 2001) or correlation-based
methods (Cohen et al. 2000). High-resolution mapping of human exon
boundaries using oligonucleotide arrays has also been reported
(Shoemaker et al. 2001; Kapranov et al. 2002). Microarrays could be
designed with probes more evenly spaced throughout the ORFs and the
intergenic regions to allow more comprehensive coverage of the
transcriptome. The continually increasing density of oligonucleotide
arrays suggests that transcriptome data, and our resulting
understanding of transcriptional regulation, will increase not only in
scope, but also in detail.
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METHODS
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Growth of Bacterial Strains and Transcript Inhibition
E. coli wild-type strain MG1655 was grown in LB broth
medium in shaken flasks at 37°C to mid-logarithmic phase
(A600 = 0.8) and then split into five flasks of 20 mL each.
To initiate transcription inhibition, four of these samples were
treated with rifampicin (Sigma) at a concentration of 50 µg
mL–1 and incubated for an additional 2.5, 5, 10, and 20 min
respectively, followed by immediate harvesting of the cells. The fifth
sample was used as a control, and cells were harvested immediately (at
timepoint zero). All RNA isolation procedures were accomplished with
the MasterPure Complete DNA and RNA Purification kit from Epicentre
Technologies, as described (Rosenow et al. 2001).
RNA Labeling and Hybridization
The cDNA synthesis method was described (Rosenow et al. 2001).
Briefly, 10 µg of total RNA was reverse-transcribed using the
Superscript II system for first-strand cDNA synthesis from Life
Technologies. The remaining RNA was removed using 2 U RNase H (Life
Technologies) and 1 µg RNase A (Epicentre) for 10 min at 37°C in
100 µL total volume. The cDNA was purified using the Qiaquick PCR
purification kit from QIAGEN. Isolated cDNA was quantitated based on
the absorption at 260 nm and fragmented using a partial DNase I digest.
The fragmented cDNA was 3' end-labeled using terminal transferase
(Roche Molecular Biochemicals) and biotin-N6-ddATP (DuPont/NEN). The
fragmented and end-labeled cDNA was added to the hybridization solution
without further purification. Three microarray hybridizations were
carried out for each timepoint.
Chip Scaling, Transcript Detection
To account for experimental and chip variations, all intensities
were normalized according to the variations of the cRNA controls, which
were added before the RNA labeling reaction and contained four probe
sets targeting RNAs not present in the E. coli genome. The
controls show a variation of less than 10% before scaling for all 15
labeling reactions (data not shown). Transcript abundances for each RNA
were calculated in GAPS by taking a mean of the perfect match (PM)
minus mismatch (MM) probes, after removing the highest and two lowest
(2–13 max.; Selinger et al. 2000) and are referred to here simply as
"average difference" (AD; Lockhart et al. 1996). Each RNA is
typically targeted by 15 unique oligonucleotide probe pairs. A
transcript was considered "detected" if it was 2.33 (99%
confidence) above the negative controls (90 probe sets for genes not
present in the MG1655 genome). For the five timepoints (0, 2.5, 5, 10,
and 20 min), mRNA detection rates were 24%, 27%, 27%, 18%, and 6%,
respectively, with detection cutoffs of 1766, 1014, 975, 1202, and 1327
AD units. The mean of the negative controls has been subtracted from
all reported values, so that values greater than 0 signify an average
difference greater than the negative controls. For high-resolution
analysis (including the directionality analysis), we calculated
log2 ratios as log2([PM–MM of time t]/[PM–MM
of time 0]). We only used probe pairs in which PM–MM at time 0 was
greater than 100 normalized fluorescent units.
RNA Chemical Half-Life Determination
Probe pairs (perfect match – mismatch) were averaged over the
triplicates of each timepoint (0, 2.5, 5, 10, and 20 min after
rifampicin addition), resulting in an average probe set intensity for
each ORF. RNA abundances were determined using the average difference
metric implemented by GAPS©. Chemical half-life was
determined for each RNA by the following "twofold" algorithm: (1)
The earliest timepoint at which the transcript was detected was used as
the baseline abundance. (2) The earliest successive timepoint for which
a twofold decrease was detected was used as the experimental abundance,
and the half-life was calculated assuming exponential decay. When the
baseline but not the experimental timepoint was detected, the half-life
was estimated (yielding an upper-bound estimate) using the noise value
in place of the experimental value. Other categories were defined, such
as "stable" (transcript is detected but no change as great as
twofold observed), "possible increase" (a minimum twofold change
between any two timepoints), "erratic" (both a twofold increase and
decrease observed), and "possibly stable" (at least a twofold
decrease observed, but later returns to baseline level). Slot blots for
four genes were carried out as a validation of the array-measured RNA
half-lives, giving the following results (slot blot/array):
ompA 20.2 min/stable; cspC 17.2 min/possibly stable;
fldA 10 min/6.7 min; sodA 9.5 min/6.9 min. Half-lives
were alternatively calculated by fitting an exponential decay curve to
all timepoints, regardless of fold change or signal-to-noise
thresholds. This approach was deemed inferior to the twofold algorithm,
because it gave considerably poorer agreement with slot blot data,
showed less sensitivity to rapidly degrading transcripts, and gave
spurious results for RNAs whose signal dropped below the detection
threshold at later timepoints (data not shown). Average half-lives were
calculated for predicted and observed operons from RegulonDB (Salgado
et al. 2001) by taking a mean for all operon members whose half-life
had been determined. Half-lives with estimated upper bounds of greater
than 40 min were set equal to 40 min to avoid skewing the results. The
complete list of transcripts, calculated half-lives (of both ORFs and
operons), and pattern categories are available at
http://arep.med.harvard.edu/rna_decay/. The dataset was also
deposited in ExpressDB (Aach et al. 2000) at
http://arep.med.harvard.edu/ExpressDB/.
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WEB SITE REFERENCES
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http://arep.med.harvard.edu/rna_decay/; RNA decay data and
half-life.
http://arep.med.harvard.edu/ExpressDB/; Relational database containing
yeast and E. coli RNA expression data.
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Acknowledgements
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We thank Sidney Kushner for advice and the provision of mutants
(not used in this study), and Kenn Rudd and Joel Belasco for critical
reviews of the manuscript. D.S. was graciously hosted in the lab of
Minoru Kanehisa for part of this work. This work was supported by
grants from the NSF-MEXT Monbusho program, Lipper Foundation, NSF, and
DOE.
The publication costs of this article were defrayed in
part by payment of page charges. This article must therefore be hereby
marked "advertisement" in accordance with 18 USC section 1734
solely to indicate this fact.
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Footnotes
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3 These authors contributed equally to this work. 
4 Corresponding author.
E-MAIL carsten_rosenow{at}affymetrix.com; FAX (408) 481-0422.
Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.912603. Article published online before print in January 2003.
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Received June 5, 2002;
accepted in revised format November 20, 2002.
13:216-223 © by 2003 Cold Spring Harbor Laboratory Press ISSN 1088-9051/03 $5.00
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ABSTRACT
RESULTS AND DISCUSSION